Heuristic methods of fuzzy clustering hold a special place in data mining. They are important in preliminary data analysis when the number of clusters, their structure and mutual arrangement are unknown. The object clustering methods, based on fuzzy binary relations are generalized by developing clear and fuzzy single-level clustering methods, clear and fuzzy sequential multi-level clustering methods. Possible examples of fuzzy binary relations, which characterize similarity of objects by length, angle and distance of their vector features are presented. For this purpose, the Harrington type desirability function and scale, enabling effective analysis of clustering results are suggested. Based on the proposed methods, the software systems that were effectively used for solving applied clustering problems are developed. Also, the study illustrated the clear single-level clustering method on a specific example. It is shown that application of the apparatus of fuzzy binary relations in clustering provides an additional opportunity to study the dynamics of the number of clusters, their structure and determine the degree of similarity of objects in a cluster. The results can be used for preliminary data analysis and for holding the object clustering procedure.